Title

Author

Date of Award

Level of Access

Campus-Only Thesis

Degree Name

Master of Science (MS)

Department

Electrical and Computer Engineering

Advisor

Mohamad T. Musavi

Second Committee Member

Habtom Ressom

Third Committee Member

Sean Ireland

Abstract

Principal Components Analysis (PCA) is a conventional linear technique for projecting multidimensional data onto lower dimensional spaces with minimal loss of variance. However, there are several applications where the data is not linear; in these cases linear PCA is not the optimal method to recover this subspace and thus account for the largest proportion of variance in the data. In this thesis, a non-linear PCA (IVLPCA) method is developed using a new technique that combines Radial Basis function with Particle Swarm optimization. The new technique is evaluated and compared to other standard methods in the applications of function approximation, feature extraction, and process monitoring.